The present disclosure relates in general to the field of image data analytics. More specifically, the present disclosure relates to systems and methodologies for using social media image content to identify and visualize social media topics over time.
The high volume and high variability of content posted on social media web sites make it challenging to create a visualization framework that can accurately and meaningfully capture social media topics related both to the facts of real-world events, and to the stories about the real-world events that develop through social media activity. Known approaches to creating such a visualization framework include performing sentiment analysis or determining the most frequently posted terms and phrases. Either approach has shortcomings. For example, the simple computation of frequently posted terms/phrases can easily and inadvertently mix together terms from different topics. Semantic analysis can provide holistic, high level views of the sentiment for a set of posts but lack depth and granularity. To the extent these known approaches attempt to consider images, the focus is on clustering similar images and not one connecting the images to the temporal occurrence of topics.